### EE 325 – PROBABILITY AND RANDOM PROCESSES

**Semester:**

Autumn 2015

**Instructor:**

Prof. Gaurav Kasbekar

**Prerequisites:**

EE-223- Data analysis and interpretation

**Motivation for the course:**

Probability and random processes is integral to signal processing, communications and machine learning. It finds wide application across diverse fields beyond elec, including economics, finance etc.

**Course Content:**

Review of classical probability; Different definitions, axioms of probability; Review of set theory, Infinite sets; Fields; Conditional probability and independence; Continuous, discrete and other Random variables; Cumulative distribution functions; Conditional distributions; Functions of random variables; Random vectors; Joint normal distributions; Inequalities; Characteristic functions; Random sequences and their convergence; Random Processes: basics, stationary processes, mean correlation and covariance functions, ergodicity, through LTI systems, Power spectral density, Gaussian processes, Noise

**Lectures:**

The lectures were extremely well structured. All the concepts, proofs and examples were meticulously explained on the blackboard alongside the presentation. Lectures weren’t inherently interactive and participation from the students, on occasions, was restricted to doubts and queries raised by the students. The instructor was very receptive to doubts.

Try to be on time for the lectures, or the remaining lecture would be hard to follow. Some parts on set theory and fields, random sequences and processes can be hard to grasp. Be patient with the course and feel free to meet the instructor for help even outside class hours. He’s very patient with his explanations. Slides were uploaded by the instructor right after the lecture, solutions uploaded immediately after the exams.

**Attendance:**

Manual attendance was taken everyday; yet the attendance in the course was consistently very low. Though no one was penalised for missing lectures, attending all lectures is recommended as the concepts are a little difficult to grasp.

Advertisements

REPORT THIS AD

Try not to use the abundance of supporting material uploaded by the instructor as a reason for missing lectures. Detailed examples and proofs were worked out in the class, with only the summary mentioned in the slides. This is one course, which you will have to keep revisiting during subsequent courses in communications and signal processing.

**Homework Problems:**

Plenty of homework problem sheets were uploaded regularly. Detailed solutions were also posted after a week or so. The assignments were neither collected or graded. As a result, a large no of students ended up solving these assignments right before the exam day. Regularly solving assignments and approaching the instructor for doubts is highly recommended.

**Exams:**

20% Quizzes (best 2 of 3, 10 marks each covering 1/3 of the syllabus)

30% Midsem (30 marks)

50% Endsem (50 marks)

The question papers were fairly balanced. But, in the absence of regular submissions and the persisting low attendance, a lot of students resorted to last moment preparation. The class performance in the first quiz was poor. Class average remained low (9.5/30) in the mid-sem, following which the instructor reduced the level of difficulty. The last 2 quizzes and end-sem were very scoring, with a couple of questions straight out of the homework. If you’re solving the assignments regularly, you’ll comfortably sail through the exams.

**Grading:**

The batch was divided into 2 sections. For Btech students, Prof Chaporkar took the course. Section wise break-up isn’t available on asc. The grading was harsher for the Btech section. For us, no DX Grades, hardly any FRs were given. People who performed very poorly in the midsems were called for a meeting with the HOD. High grades(AA, AB) were only given if the course total exceeded 70. Overall, the grading was moderate.

**Reference Books:**

Probability, Random Variables and Stochastic Processes, Papoulis

Probability and Random Processes with Applications to Signal Processing, Stark and Woods

Probability and Random Processes, Grimmett & Strizaker

Communication Systems 4th Edition, Haykin

After every lecture, the Prof mentioned the sections that need to be referred to. There was an abundance of reference material and solved assignments which were regularly uploaded by the instructor.

For details on the **future application** of the course, you can refer to: https://dampeeiitb.wordpress.com/course-reviews/ee-325-2/

**Reviewed by:**

Mansi Sood, 4th year Undergrad, DD CSP (mansisood01@gmail.com)